SLAM

Table of Contents

1

1.1 Meta Notes

1.2 Books

1.2.1 DONE Combo #1

  1. DONE 金字塔原理
  2. TODO Beast Machines: Transformers (cartoon)
  3. DONE 论持久战
  4. DONE 一看就懂的经济常识全图解
  5. DONE 刻意练习
  6. DONE 系统之美
  7. DONE 邓小平时代

1.2.2 INPROGRESS Combo #2

  1. DONE 新经济, 新规则 (Kevin Kelly)
  2. DONE 麦肯锡问题解决方法与技巧
  3. DONE 国产遥感卫星进展与应用

1.2.5 INPROGRESS Effective Java

1.2.6 DONE Don't Make Me Think

1.2.7 DONE Clean Code

1.2.8 INPROGRESS Deep Learning

1.2.9 INPROGRESS Async JavaScript

1.2.10 INPROGRESS ng-book2

1.2.11 INPROGRESS Combo #3: SLAM

2 GeekLiB/Lee-SLAM-source: SLAM 开发学习资源与经验分享

3 Multiple View Geometry in Computer Vision

3.1 1. Introduction - a tour of multiple view geometry;

3.2 Part 0. The Background: Projective Geometry, Transformations and Estimation: (2018-05-01 ~ 2018-05-05)

3.2.1 2. Projective geometry and transformations of 2D;

3.2.2 3. Projective geometry and transformations of 3D;

3.2.3 4. Estimation - 2D projective transforms;

3.2.4 5. Algorithm evaluation and error analysis;

3.3 Part I. Camera Geometry and Single View Geometry: (2018-05-06 ~ 2018-05-12)

3.3.1 6. Camera models;

3.3.2 7. Computation of the camera matrix;

3.3.3 8. More single view geometry;

3.4 Part II. Two-View Geometry: (2018-05-13 ~ 2018-05-19)

3.4.1 9. Epipolar geometry and the fundamental matrix;

3.4.2 10. 3D reconstruction of cameras and structure;

3.4.3 11. Computation of the fundamental matrix F;

3.4.4 12. Structure computation;

3.4.5 13. Scene planes and homographies;

3.4.6 14. Affine epipolar geometry;

3.5 Part III. Three-View Geometry: (2018-05-20 ~ 2018-05-26)

3.5.1 15. The trifocal tensor;

3.5.2 16. Computation of the trifocal tensor T;

3.6 Part IV. N -View Geometry: (2018-05-27 ~ 2018-06-02)

3.6.1 17. N-linearities and multiple view tensors;

3.6.2 18. N-view computational methods;

3.6.3 19. Auto-calibration;

3.6.4 20. Duality; 21. Chirality;

3.6.5 22. Degenerate configurations;

3.7 Part V. Appendices:

3.7.1 Appendix 1. Tensor notation;

3.7.2 Appendix 2. Gaussian (normal) and chi-squared distributions;

3.7.3 Appendix 3. Parameter estimation.

3.7.4 Appendix 4. Matrix properties and decompositions;

3.7.5 Appendix 5. Least-squares minimization;

3.7.6 Appendix 6. Iterative Estimation Methods;

3.7.7 Appendix 7. Some special plane projective transformations;

3.7.8 Bibliography;

4 State Estimation for Robotics

4.1 1. Introduction (1~3)

4.1.1 A Little History

4.1.2 Sensors, Measurements, and Problem Definition

4.1.3 How This Book is Organized

4.1.4 Relationship to Other Books

{{{Part I: Estimation Machinery

4.2 2. Primer on Probability Theory (1~3)

4.2.1 Probability Density Functions

  1. Definitions
  2. Bayes' Rule and Inference
  3. Moments
  4. Sample Mean and Covariance
  5. Statistically Independent, Uncorrelated
  6. Normalized Product
  7. Shannon and Mutual Information
  8. Cramer-Rao Lower Bound and Fisher Information

4.2.2 Gaussian Probability Density Functions

  1. Definitions
  2. Isserlis' Theorem
  3. Joint Gaussian PDFs, Their Factors ,and Inferecnce
  4. Statistically Independent, Uncorrelated
  5. Linear Change of Variables
  6. Normalized Product of Gaussians
  7. Sherman-Morrison-Woodbury Identity
  8. Passing a Gausion throught a Nonlinearity
  9. Shannon Information of a Gaussian
  10. Mutual Information of a Joint Gaussian PDF
  11. Cramer-Rao Lower Bound Applied to Gaussion PDFs

4.2.3 Gaussian Processes

4.2.4 Summary

4.2.5 Exercies

4.3 3. Linear-Gaussian Estimation (1~3)

4.3.1 Batch Discrete-Time Estimation

  1. Problem Setup
  2. Maximum A Posteriori
  3. Bayesian Inference
  4. Existence,Uniqueness , and Observability
  5. MAP Convariance

4.3.2 Recursive Discrete-Time Smoothing

  1. Exploiting Sparsity in the Batch Solution
  2. Cholesky Smoother
  3. Rauch-Tung-Strieble Smoother

4.3.3 Recursice Discrete-Time Filtering

  1. Factoring the Batch Solution
  2. Kalman Fileter via MAP
  3. Kalman Filter via Bayesian Inference
  4. Kalman Filter via Gain Optimization
  5. Kalman Filter Discussion
  6. Error Dynamics
  7. Existence, Uniqueness, and Observability

4.3.4 Batch Continuous-Time Estimation

  1. Gaussian Process Regression
  2. A Class of Exactly Sparse Gaussian Process Priors
  3. Linear Time-Invariant Case
  4. Relationship to Batch Discrete-Tme Estimation

4.3.5 Summary

4.3.6 Exercises

4.4 4. Nonlinear Non-Gaussian Estimation (4~7)

4.4.1 Introduction

  1. Full Bayesian Estimation
  2. Maximum a Posteriori Estimation

4.4.2 Recursive Discrete-Time Estimation

  1. Problem Setup
  2. Bayes Filter
  3. Extended Kalman Filter
  4. Generalized Gaussian Filter
  5. Iterated Extented Kalman Filter
  6. IEKF Is a MAP Estimator
  7. Alternatives for Passing PDFs through Nonlinearities
  8. Particle Filter
  9. Sigmapoint Kalman Filter
  10. Iterated Sigmapoint Kalman Filter
  11. ISPKF Seeks the Posterior Mean
  12. Taxonomy of Filters

4.4.3 Batch Discrete-Time Estimation

  1. Maximum A Posteriori
  2. Bayessian Inference
  3. Maximum A Posteriori
  4. Discussion

4.4.4 Batch Continuous-Time Estimation

  1. Motion Model
  2. Observation Model
  3. Bayesian Inference
  4. Algorithm Summary

4.4.5 Summary

4.4.6 Exercises

4.5 5. Biases, Correspondences, and Outliers (4~7)

4.5.1 Handling Input/Measurement Biases

  1. Bias Effects on the Kalman Filter
  2. Unknown Input Bias
  3. Unknown Measurment Bias

4.5.2 Data Association

  1. External Data Association
  2. Internal Data Association

4.5.3 Handling Outliers

  1. RANSAC
  2. M-Estimation
  3. Covariance Estimation

4.5.4 Summary

4.5.5 Exercises

Part II Three-Dimensional machery

{{{Part II: Three-Dimensional machinery

4.6 6. Primer on Three-Dimensional Geometry (4~7)

4.6.1 Vectors and Renference Frames

  1. Reference Frames
  2. Dot Product
  3. Cross Product

4.6.2 Rotations

  1. Rotation Matrices
  2. Principal Rotations
  3. Alternate Rotation Representations
  4. Rotational Kinematics
  5. Perturbing Rotations

4.6.3 Poses

  1. Transformation Matrices
  2. Robotcs Conventions
  3. Frenet-Serret Frame

4.6.4 Senor Models

  1. Perspective Camera
  2. Stereo Camera
  3. Range-Azimuth-Elevation
  4. Inertial Measument Unit

4.6.5 Summary

4.6.6 Exercises

4.7 7. Matrix Lie Groups (4~7)

4.7.1 Geometry

  1. Special Orthogonal and Special Euclidean Groups
  2. Lie Algebras
  3. Exponentials Map
  4. Adjoints
  5. Baker-Compbell-Hausdorff
  6. Distance, Volume, Integration
  7. Interpolation
  8. Homogeneous Points
  9. Calculus and Optimation
  10. Identities

4.7.2 Kinematics

  1. Rotations
  2. Poses
  3. Linearized Rotations
  4. Linearized Poses

4.7.3 Probabilty and Statistics

  1. Gaussian Random Variables and PDFs
  2. Uncertainty on a Rotated Vector
  3. Compounding Poses
  4. Fusing Poses
  5. Propagating Uncertainty through a Nonlinear Camera Model

4.7.4 Summary

4.7.5 Exercises

Part III Applications

{{{Part III: Applications

4.8 8. Pose Estimation Problems (8~10)

4.8.1 Point-Cloud Alignment

  1. Problem Setup
  2. Unit-Length Quaternion Solution
  3. Rotation Matrix Solution
  4. Transformation Matrix Solution

4.8.2 Point-Clound Tracking

  1. Problem Setup
  2. Motion Priors
  3. Measumement Model
  4. Batch Maximum a Posteriori Solution

4.8.3 Pose-Graph Relaxation

  1. Problem Setup
  2. Batch Maximum Likelihood Solution
  3. Initialization
  4. Exploiting Sparsity
  5. Chain Example

4.9 9. Pose-and-Point Estimation Problems (8~10)

4.9.1 Bundle Adjustment

  1. Problem Setup
  2. Mealument Model
  3. Maxmim Likelihood Solution
  4. Exploiting Sparsity
  5. Interpolation Example

4.9.2 Simultaneous Localization and Mapping

  1. Problem Setup
  2. Batch maxmimum a Posteriori Solution
  3. Exploiting Sparsity
  4. Example

4.10 10. Continuous-Time Estimation (8~10)

4.10.1 Motion Prior

  1. General
  2. Simplification

4.10.2 Simultaneous Trajectory Estimation and Mapping

  1. Problem Setup
  2. Measurement Model
  3. Batch Maximum a Posteriori Solution
  4. Exploiting Sparsity
  5. Interpolation
  6. Postscript

Author: TANG ZhiXiong

Created: 2018-05-06 日 17:39

Emacs 25.3.1 (Org mode 8.2.10)

Validate